Treatment of Primary Progressive Aphasias by Transcranial Direct Current Stimulation Combined with Language Training
Bibliographic record
Abstract
BACKGROUND: Primary progressive aphasia (PPA) is an untreatable neurodegenerative disorder that disrupts language functions. Previous studies have demonstrated transcranial direct current stimulation (tDCS) may improve language symptoms in patients with post stroke aphasia or neurodegenerative diseases. OBJECTIVE: The present study investigated whether the application of anodal tDCS (AtDCS) to the scalp overlying the left dorsolateral prefrontal cortex (DLPFC), which may increase cortical excitability, in combination with individualized speech therapy would improve naming accuracy in the agrammatic variant of PPA (avPPA). METHODS: Sixteen avPPA patients were randomly allocated into two subgroups: AtDCS (n = 8) or placebo tDCS (PtDCS). tDCS was applied over the left DLPFC (BA 8/9) 25 minutes per day for two weeks (10 days). Each patient underwent 25 minutes of individualized speech therapy with either AtDCS or PtDCS during each treatment session. Neuropsychological assessment, experimental naming, and linguistic abilities in daily living were assessed at baseline (T0), after two weeks of intervention (T1) and at a 12-week follow-up (T2). RESULTS: Significant improvement in experimental naming was observed in both groups at T1 and T2, but this effect was significantly greater in AtDCS than PtDCS patients. Naming correctness, as assessed using the Aachener Aphasie Test, increased selectively in the AtDCS group from T0 to T1, and this effect remained significant at T2. The analysis of daily living language abilities improved selectively in AtDCS group. CONCLUSION: Our results support the beneficial effect of targeted language training in combination with brain stimulation in avPPA patients. tDCS should be considered a useful tool for the improvement of language functions in patients with neurodegenerative diseases in future trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".